Gain insights into using Python Pandas for efficient data analysis and manipulation. Delve into detailed functions, parameters, and methods to elevate your data handling skills.

A Comprehensive Practical Guide to Python Pandas_ .png

There are several exercises that focus on how to use a particular function and method. The functions are covered in detail by explaining the important parameters and how to use them. By completing this course, you will be able to do data analysis and manipulation with Pandas easily and efficiently.

Mastering Data Analysis with Python Pandas

# Problem statement

Up to this point, we’ve covered:

* How to find the number of unique values in a column.
* How to find the average value of a column.
* How to filter the values in a `DataFrame`.

The challenge is to find the number of products that are priced higher than the average product price in the sales data frame. As a hint, keep in mind that you need to use the operations specified with the bullet points above.

# Solution

Write your solution in the specified place and click on "test".

* Make sure to uncomment the lines before execution.

Learn how to find values below average in a column of a DataFrame.

Course Introduction

Pandas Data Structures

Creating a Data Frame

Exploring a Data Frame

Filtering a Data Frame

String Manipulation with Pandas

Date Manipulation with Pandas

Handling Missing Values with Pandas

Data Analysis with Pandas

Data Visualization with Pandas

Combining DataFrames with Pandas

Data Visualization with Seaborn for Walmart Sales Projection

Final Challenge and Quiz

Challenge: Filter Out the Values Below Average

Problem statement